文献类型：期刊文献

英文题名：STGFP: information enhanced spatio-temporal graph neural network for traffic flow prediction

作者：Li, Qi[1];Wang, Fan[1];Wang, Chen[2]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing 312000, Zhejiang, Peoples R China;[2]Chongqing Univ, Sch Comp Sci, Chongqing 400044, Peoples R China

年份：2025

卷号：55

期号：6

外文期刊名：APPLIED INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:001434431500008)、、EI(收录号：20251017999464)、Scopus(收录号：2-s2.0-85219597781)、WOS

基金：This work was supported by Natural Sciences Foundation of Zhejiang Province under Grant No. LY22F020003.

语种：英文

外文关键词：Traffic flow prediction; Graph neural network; Information enhanced; Attention mechanism; Non-Euclidean structure

外文摘要：Accurate traffic flow prediction is crucial for the development of intelligent transportation systems aimed at preventing and mitigating traffic issues. We present an information-enhanced spatio-temporal graph neural network model to predict traffic flow, addressing the inefficient utilization of non-Euclidean structured traffic data. Firstly, we employ a multivariate temporal attention mechanism to capture dynamic temporal correlations across different time intervals, while a second-order graph attention network identifies spatial correlations within the network. Secondly, we construct two types of traffic topology graphs that comprehensively describe traffic flow features by integrating non-Euclidean traffic flow data, regional traffic status information, and node features. Finally, a multi-graph convolution neural network is designed to extract long-range spatial features from these traffic topology graphs. The spatio-temporal feature extraction module then combines these long-range spatial features with spatio-temporal features to fuse multiple features and improve prediction accuracy. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baseline methods in predicting traffic flow performance.